The Computational Core of the CCNE has been designed with two major aims in mind: (1) providing a data capture, fusion, and sharing environment with large-scale automation and an integrated software stack, and (2) discovering incipient cancer, and pre- and post-treatment temporal monitoring of its evolution by analyzing captured time series of biological processes using multivariate state estimation techniques and other kernel-based technologies for the first time. The realization of global data sharing, analysis, and coherence requires software (not hardware) architecture that is essentially an integrated stack interfacing any data capture device with routers, computers, network management software, and databases, all of which are connected to the internet but not yet able to communicate. Our core will show how this architecture is realized and use it with our particular project data to find incipient disease using sophisticated algorithms and a great deal of medical intuition from our team. In time, our nanosensor technologies will be created and integrated into the data capture and intervention cycle, yielding even greater temporal resolution and therefore, higher probability of accurate and specific disease identification and treatment. There are two high impact benefits of successful work: (1) ubiquitous access to global data through the internet physical layer in the coming years, and (2) a new method for collecting and analyzing data to find incipient disease.